You can't use a USB drive as VRAM — the enterprise guide to GPU memory capacity planning in 2026
Storage isn't VRAM. eGPUs aren't a data center strategy. Shared memory isn't a capacity plan. Every quarter, an AI infrastructure team somewhere asks the same question: "we're running out of GPU memory, can we just use the SSDs?" No. Here's what actually works at scale, what doesn't, and the procurement and capacity planning playbook for GPU memory in 2026.
This post is written for AI platform engineers, infrastructure architects, and FinOps leads running shared GPU clusters. It's not about a single workstation — it's about a fleet. The unit of analysis is the rack, the budget, and the quarter.
Once the fleet exists, two companion posts cover what runs on it: engine selection and quantization (which engine, which precision, when to call an API instead) and GPU Autoscaling is Broken (scaling LLM inference under real load). This one is the layer above both: how much GPU memory to buy in the first place.